Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations154
Missing cells130
Missing cells (%)3.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.9 KiB
Average record size in memory172.0 B

Variable types

Categorical1
DateTime1
Numeric18
Boolean4

Alerts

colif_fecales_ufc_100ml is highly overall correlated with icaHigh correlation
color is highly overall correlated with sitiosHigh correlation
dbo_mg_l is highly overall correlated with fosf_ortofos_mg_l and 2 other fieldsHigh correlation
espumas is highly overall correlated with sitiosHigh correlation
fosf_ortofos_mg_l is highly overall correlated with dbo_mg_l and 2 other fieldsHigh correlation
ica is highly overall correlated with colif_fecales_ufc_100ml and 4 other fieldsHigh correlation
microcistina_ug_l is highly overall correlated with tem_agua and 1 other fieldsHigh correlation
od is highly overall correlated with phHigh correlation
olores is highly overall correlated with icaHigh correlation
p_total_l_mg_l is highly overall correlated with dbo_mg_l and 2 other fieldsHigh correlation
ph is highly overall correlated with odHigh correlation
sitios is highly overall correlated with color and 1 other fieldsHigh correlation
tem_agua is highly overall correlated with microcistina_ug_l and 1 other fieldsHigh correlation
tem_aire is highly overall correlated with microcistina_ug_l and 1 other fieldsHigh correlation
olores is highly imbalanced (60.5%) Imbalance
color is highly imbalanced (58.2%) Imbalance
espumas is highly imbalanced (79.3%) Imbalance
fecha has 2 (1.3%) missing values Missing
tem_agua has 10 (6.5%) missing values Missing
tem_aire has 11 (7.1%) missing values Missing
od has 23 (14.9%) missing values Missing
ph has 15 (9.7%) missing values Missing
p_total_l_mg_l has 7 (4.5%) missing values Missing
dbo_mg_l has 44 (28.6%) missing values Missing
cr_total_mg_l has 3 (1.9%) missing values Missing
clorofila_a_ug_l has 4 (2.6%) missing values Missing
microcistina_ug_l has 8 (5.2%) missing values Missing
sitios is uniformly distributed Uniform
clorofila_a_ug_l has 5 (3.2%) zeros Zeros

Reproduction

Analysis started2024-11-01 20:58:49.032547
Analysis finished2024-11-01 21:01:03.858111
Duration2 minutes and 14.83 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

sitios
Categorical

High correlation  Uniform 

Distinct41
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
Canal Villanueva y Río Luján
 
4
Río Lujan y Arroyo Caraguatá
 
4
Canal Aliviador y Río Lujan
 
4
Río Carapachay y Arroyo Gallo Fiambre
 
4
Río Reconquista y Río Lujan
 
4
Other values (36)
134 

Length

Max length41
Median length27
Mean length23.415584
Min length8

Characters and Unicode

Total characters3606
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCanal Villanueva y Río Luján
2nd rowRío Lujan y Arroyo Caraguatá
3rd rowCanal Aliviador y Río Lujan
4th rowRío Carapachay y Arroyo Gallo Fiambre
5th rowRío Reconquista y Río Lujan

Common Values

ValueCountFrequency (%)
Canal Villanueva y Río Luján 4
 
2.6%
Río Lujan y Arroyo Caraguatá 4
 
2.6%
Canal Aliviador y Río Lujan 4
 
2.6%
Río Carapachay y Arroyo Gallo Fiambre 4
 
2.6%
Río Reconquista y Río Lujan 4
 
2.6%
Rio Tigre 100m antes del Rio Luján 4
 
2.6%
Río Lujan y Canal San Fernando 4
 
2.6%
Río Capitán y Río San Antonio 4
 
2.6%
Arroyo Abra Vieja y Santa Rosa 4
 
2.6%
Del Arca 4
 
2.6%
Other values (31) 114
74.0%

Length

2024-11-01T18:01:04.649205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
y 40
 
6.3%
río 36
 
5.7%
de 27
 
4.3%
arroyo 24
 
3.8%
lujan 16
 
2.5%
costa 12
 
1.9%
canal 12
 
1.9%
m 12
 
1.9%
400 12
 
1.9%
calle 11
 
1.7%
Other values (93) 428
67.9%

Most occurring characters

ValueCountFrequency (%)
476
 
13.2%
a 442
 
12.3%
o 294
 
8.2%
r 208
 
5.8%
e 205
 
5.7%
n 180
 
5.0%
l 178
 
4.9%
i 145
 
4.0%
s 103
 
2.9%
c 97
 
2.7%
Other values (47) 1278
35.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3606
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
476
 
13.2%
a 442
 
12.3%
o 294
 
8.2%
r 208
 
5.8%
e 205
 
5.7%
n 180
 
5.0%
l 178
 
4.9%
i 145
 
4.0%
s 103
 
2.9%
c 97
 
2.7%
Other values (47) 1278
35.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3606
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
476
 
13.2%
a 442
 
12.3%
o 294
 
8.2%
r 208
 
5.8%
e 205
 
5.7%
n 180
 
5.0%
l 178
 
4.9%
i 145
 
4.0%
s 103
 
2.9%
c 97
 
2.7%
Other values (47) 1278
35.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3606
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
476
 
13.2%
a 442
 
12.3%
o 294
 
8.2%
r 208
 
5.8%
e 205
 
5.7%
n 180
 
5.0%
l 178
 
4.9%
i 145
 
4.0%
s 103
 
2.9%
c 97
 
2.7%
Other values (47) 1278
35.4%

fecha
Date

Missing 

Distinct4
Distinct (%)2.6%
Missing2
Missing (%)1.3%
Memory size2.4 KiB
Minimum2022-02-23 00:00:00
Maximum2022-10-31 00:00:00
2024-11-01T18:01:04.899712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:01:05.118596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)

tem_agua
Real number (ℝ)

High correlation  Missing 

Distinct90
Distinct (%)62.5%
Missing10
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean17.763403
Minimum6
Maximum27.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-01T18:01:05.368547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile10
Q114.6
median17.85
Q320.4
95-th percentile25.655
Maximum27.4
Range21.4
Interquartile range (IQR)5.8

Descriptive statistics

Standard deviation4.8108313
Coefficient of variation (CV)0.27082825
Kurtosis-0.38181946
Mean17.763403
Median Absolute Deviation (MAD)3
Skewness-0.079207544
Sum2557.93
Variance23.144098
MonotonicityNot monotonic
2024-11-01T18:01:05.681659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 7
 
4.5%
20 6
 
3.9%
18.5 5
 
3.2%
18.6 5
 
3.2%
17 4
 
2.6%
23 3
 
1.9%
15.6 3
 
1.9%
24.7 3
 
1.9%
24.5 3
 
1.9%
18.2 3
 
1.9%
Other values (80) 102
66.2%
(Missing) 10
 
6.5%
ValueCountFrequency (%)
6 1
 
0.6%
7 2
 
1.3%
8 2
 
1.3%
9 1
 
0.6%
10 7
4.5%
10.01 1
 
0.6%
11 1
 
0.6%
11.01 1
 
0.6%
12 1
 
0.6%
12.7 2
 
1.3%
ValueCountFrequency (%)
27.4 1
0.6%
27 1
0.6%
26.3 1
0.6%
26.1 2
1.3%
26 1
0.6%
25.8 1
0.6%
25.7 1
0.6%
25.4 2
1.3%
25.2 1
0.6%
25.1 1
0.6%

tem_aire
Real number (ℝ)

High correlation  Missing 

Distinct29
Distinct (%)20.3%
Missing11
Missing (%)7.1%
Infinite0
Infinite (%)0.0%
Mean15.714685
Minimum4
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-01T18:01:05.932009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile8
Q113
median14
Q318.5
95-th percentile25.2
Maximum27
Range23
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation5.0550206
Coefficient of variation (CV)0.32167495
Kurtosis-0.22865991
Mean15.714685
Median Absolute Deviation (MAD)2
Skewness0.46806325
Sum2247.2
Variance25.553234
MonotonicityNot monotonic
2024-11-01T18:01:06.198500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
14 25
16.2%
13 15
 
9.7%
12 12
 
7.8%
16 11
 
7.1%
17 9
 
5.8%
15 7
 
4.5%
22 6
 
3.9%
10 5
 
3.2%
8 5
 
3.2%
27 5
 
3.2%
Other values (19) 43
27.9%
(Missing) 11
 
7.1%
ValueCountFrequency (%)
4 1
 
0.6%
5 1
 
0.6%
6 1
 
0.6%
7 1
 
0.6%
8 5
3.2%
9 3
 
1.9%
10 5
3.2%
11 4
 
2.6%
12 12
7.8%
12.3 1
 
0.6%
ValueCountFrequency (%)
27 5
3.2%
26 1
 
0.6%
25.2 3
1.9%
25 1
 
0.6%
23.3 5
3.2%
23 5
3.2%
22.2 2
 
1.3%
22 6
3.9%
21 2
 
1.3%
20 4
2.6%

od
Real number (ℝ)

High correlation  Missing 

Distinct126
Distinct (%)96.2%
Missing23
Missing (%)14.9%
Infinite0
Infinite (%)0.0%
Mean6.7583969
Minimum0.36
Maximum17.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-01T18:01:06.464060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.36
5-th percentile1.985
Q15.06
median6.7
Q38.59
95-th percentile10.95
Maximum17.61
Range17.25
Interquartile range (IQR)3.53

Descriptive statistics

Standard deviation2.8437962
Coefficient of variation (CV)0.42077969
Kurtosis0.83480143
Mean6.7583969
Median Absolute Deviation (MAD)1.8
Skewness0.21128341
Sum885.35
Variance8.0871766
MonotonicityNot monotonic
2024-11-01T18:01:06.777126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.85 2
 
1.3%
4.28 2
 
1.3%
9 2
 
1.3%
5.36 2
 
1.3%
7 2
 
1.3%
2.22 1
 
0.6%
1.5 1
 
0.6%
6.3 1
 
0.6%
3.85 1
 
0.6%
4.49 1
 
0.6%
Other values (116) 116
75.3%
(Missing) 23
 
14.9%
ValueCountFrequency (%)
0.36 1
0.6%
0.45 1
0.6%
1.02 1
0.6%
1.13 1
0.6%
1.39 1
0.6%
1.5 1
0.6%
1.8 1
0.6%
2.17 1
0.6%
2.22 1
0.6%
2.25 1
0.6%
ValueCountFrequency (%)
17.61 1
0.6%
12.84 1
0.6%
12.15 1
0.6%
12 1
0.6%
11.82 1
0.6%
11.05 1
0.6%
11.02 1
0.6%
10.88 1
0.6%
10.83 1
0.6%
10.6 1
0.6%

ph
Real number (ℝ)

High correlation  Missing 

Distinct101
Distinct (%)72.7%
Missing15
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean7.5886331
Minimum5
Maximum10.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-01T18:01:07.091496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6.558
Q17.12
median7.52
Q38.005
95-th percentile8.842
Maximum10.02
Range5.02
Interquartile range (IQR)0.885

Descriptive statistics

Standard deviation0.71866039
Coefficient of variation (CV)0.094702219
Kurtosis1.9277527
Mean7.5886331
Median Absolute Deviation (MAD)0.44
Skewness0.41661836
Sum1054.82
Variance0.51647276
MonotonicityNot monotonic
2024-11-01T18:01:07.420210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.4 7
 
4.5%
7.6 4
 
2.6%
7.58 3
 
1.9%
7.3 3
 
1.9%
7.5 3
 
1.9%
7.99 3
 
1.9%
8.02 3
 
1.9%
7.8 3
 
1.9%
7.67 3
 
1.9%
7.76 3
 
1.9%
Other values (91) 104
67.5%
(Missing) 15
 
9.7%
ValueCountFrequency (%)
5 1
0.6%
6.2 1
0.6%
6.37 1
0.6%
6.39 1
0.6%
6.48 1
0.6%
6.53 1
0.6%
6.54 1
0.6%
6.56 2
1.3%
6.59 1
0.6%
6.66 1
0.6%
ValueCountFrequency (%)
10.02 1
0.6%
9.98 1
0.6%
9.39 1
0.6%
9.17 1
0.6%
9.16 1
0.6%
9.01 1
0.6%
8.95 1
0.6%
8.83 1
0.6%
8.81 1
0.6%
8.62 1
0.6%

olores
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
False
142 
True
 
12
ValueCountFrequency (%)
False 142
92.2%
True 12
 
7.8%
2024-11-01T18:01:07.686547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

color
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
False
141 
True
 
13
ValueCountFrequency (%)
False 141
91.6%
True 13
 
8.4%
2024-11-01T18:01:07.874128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

espumas
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
False
149 
True
 
5
ValueCountFrequency (%)
False 149
96.8%
True 5
 
3.2%
2024-11-01T18:01:08.062731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

mat_susp
Boolean

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
False
127 
True
27 
ValueCountFrequency (%)
False 127
82.5%
True 27
 
17.5%
2024-11-01T18:01:08.266428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

colif_fecales_ufc_100ml
Real number (ℝ)

High correlation 

Distinct88
Distinct (%)57.5%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean86690.229
Minimum80
Maximum4200000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-01T18:01:08.532166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile264
Q11200
median4000
Q340000
95-th percentile274000
Maximum4200000
Range4199920
Interquartile range (IQR)38800

Descriptive statistics

Standard deviation381271.21
Coefficient of variation (CV)4.3980875
Kurtosis91.766626
Mean86690.229
Median Absolute Deviation (MAD)3440
Skewness8.9657969
Sum13263605
Variance1.4536774 × 1011
MonotonicityNot monotonic
2024-11-01T18:01:08.829942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000 6
 
3.9%
1000 5
 
3.2%
1400 5
 
3.2%
1800 5
 
3.2%
900 4
 
2.6%
3000 4
 
2.6%
1300 4
 
2.6%
40000 4
 
2.6%
400 3
 
1.9%
6500 3
 
1.9%
Other values (78) 110
71.4%
ValueCountFrequency (%)
80 1
0.6%
95 1
0.6%
120 1
0.6%
130 1
0.6%
150 1
0.6%
160 1
0.6%
200 1
0.6%
210 1
0.6%
300 2
1.3%
360 1
0.6%
ValueCountFrequency (%)
4200000 1
0.6%
1600000 1
0.6%
1070000 1
0.6%
740000 1
0.6%
700000 1
0.6%
420000 1
0.6%
400000 1
0.6%
280000 1
0.6%
270000 1
0.6%
240000 1
0.6%

escher_coli_ufc_100ml
Real number (ℝ)

Distinct77
Distinct (%)50.3%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean4093.3922
Minimum1
Maximum150000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-01T18:01:09.126812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.6
Q1100
median330
Q31700
95-th percentile12960
Maximum150000
Range149999
Interquartile range (IQR)1600

Descriptive statistics

Standard deviation15058.087
Coefficient of variation (CV)3.6786328
Kurtosis62.789246
Mean4093.3922
Median Absolute Deviation (MAD)312
Skewness7.3116318
Sum626289
Variance2.2674597 × 108
MonotonicityNot monotonic
2024-11-01T18:01:09.439764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 17
 
11.0%
200 15
 
9.7%
300 6
 
3.9%
600 5
 
3.2%
500 4
 
2.6%
10000 4
 
2.6%
1000 4
 
2.6%
6 4
 
2.6%
2000 3
 
1.9%
3 3
 
1.9%
Other values (67) 88
57.1%
ValueCountFrequency (%)
1 1
 
0.6%
2 2
1.3%
3 3
1.9%
4 1
 
0.6%
5 1
 
0.6%
6 4
2.6%
9 1
 
0.6%
13 1
 
0.6%
15 1
 
0.6%
16 1
 
0.6%
ValueCountFrequency (%)
150000 1
0.6%
80000 1
0.6%
50000 1
0.6%
44000 1
0.6%
35000 1
0.6%
28000 1
0.6%
15000 1
0.6%
14400 1
0.6%
12000 1
0.6%
11500 1
0.6%

enteroc_ufc_100ml
Real number (ℝ)

Distinct84
Distinct (%)54.9%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean951.70588
Minimum2
Maximum28000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-01T18:01:09.737599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q150
median300
Q3670
95-th percentile2820
Maximum28000
Range27998
Interquartile range (IQR)620

Descriptive statistics

Standard deviation3008.483
Coefficient of variation (CV)3.1611479
Kurtosis54.099694
Mean951.70588
Median Absolute Deviation (MAD)270
Skewness6.9586225
Sum145611
Variance9050970.2
MonotonicityNot monotonic
2024-11-01T18:01:10.034681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 7
 
4.5%
10 6
 
3.9%
300 6
 
3.9%
50 6
 
3.9%
20 5
 
3.2%
1500 5
 
3.2%
2 5
 
3.2%
30 4
 
2.6%
1100 3
 
1.9%
80 3
 
1.9%
Other values (74) 103
66.9%
ValueCountFrequency (%)
2 5
3.2%
3 1
 
0.6%
4 1
 
0.6%
5 2
 
1.3%
9 1
 
0.6%
10 6
3.9%
11 1
 
0.6%
20 5
3.2%
24 1
 
0.6%
27 1
 
0.6%
ValueCountFrequency (%)
28000 1
0.6%
20000 1
0.6%
12000 1
0.6%
7500 1
0.6%
5000 1
0.6%
4200 1
0.6%
4000 1
0.6%
3300 1
0.6%
2500 1
0.6%
2200 1
0.6%

nitrato_mg_l
Real number (ℝ)

Distinct84
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7038961
Minimum1.9
Maximum21.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-01T18:01:10.331881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile2
Q13.6
median5.65
Q38.675
95-th percentile13.57
Maximum21.9
Range20
Interquartile range (IQR)5.075

Descriptive statistics

Standard deviation4.0578639
Coefficient of variation (CV)0.60529934
Kurtosis1.0475266
Mean6.7038961
Median Absolute Deviation (MAD)2.5
Skewness1.1216834
Sum1032.4
Variance16.466259
MonotonicityNot monotonic
2024-11-01T18:01:10.645234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 6
 
3.9%
3.3 5
 
3.2%
1.9 4
 
2.6%
5.1 4
 
2.6%
3.9 4
 
2.6%
3.7 4
 
2.6%
5.9 4
 
2.6%
2.9 3
 
1.9%
5.2 3
 
1.9%
5.6 3
 
1.9%
Other values (74) 114
74.0%
ValueCountFrequency (%)
1.9 4
2.6%
2 6
3.9%
2.1 2
 
1.3%
2.2 1
 
0.6%
2.4 1
 
0.6%
2.5 1
 
0.6%
2.6 3
1.9%
2.7 3
1.9%
2.8 2
 
1.3%
2.9 3
1.9%
ValueCountFrequency (%)
21.9 1
0.6%
20.6 1
0.6%
16.4 1
0.6%
16.3 1
0.6%
16.2 1
0.6%
14.8 1
0.6%
14.4 1
0.6%
13.7 1
0.6%
13.5 1
0.6%
13.3 2
1.3%

nh4_mg_l
Real number (ℝ)

Distinct84
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9411558
Minimum0.049
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-01T18:01:10.959175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.049
5-th percentile0.049
Q10.1025
median0.65
Q31.675
95-th percentile7.655
Maximum23
Range22.951
Interquartile range (IQR)1.5725

Descriptive statistics

Standard deviation4.1198027
Coefficient of variation (CV)2.1223452
Kurtosis16.287446
Mean1.9411558
Median Absolute Deviation (MAD)0.59
Skewness3.9375779
Sum298.938
Variance16.972774
MonotonicityNot monotonic
2024-11-01T18:01:11.290046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05 18
 
11.7%
0.049 12
 
7.8%
2 5
 
3.2%
0.1 5
 
3.2%
0.41 5
 
3.2%
1 4
 
2.6%
1.9 3
 
1.9%
0.45 3
 
1.9%
1.3 3
 
1.9%
0.75 2
 
1.3%
Other values (74) 94
61.0%
ValueCountFrequency (%)
0.049 12
7.8%
0.05 18
11.7%
0.06 2
 
1.3%
0.08 2
 
1.3%
0.1 5
 
3.2%
0.11 2
 
1.3%
0.12 1
 
0.6%
0.14 1
 
0.6%
0.15 1
 
0.6%
0.17 1
 
0.6%
ValueCountFrequency (%)
23 2
1.3%
22 2
1.3%
19 1
0.6%
12 1
0.6%
9.3 1
0.6%
8.5 1
0.6%
7.2 1
0.6%
7.1 1
0.6%
5.7 1
0.6%
5.6 1
0.6%

p_total_l_mg_l
Real number (ℝ)

High correlation  Missing 

Distinct71
Distinct (%)48.3%
Missing7
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean0.88666667
Minimum0.1
Maximum30.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-01T18:01:11.588953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.133
Q10.26
median0.37
Q30.57
95-th percentile1.3
Maximum30.12
Range30.02
Interquartile range (IQR)0.31

Descriptive statistics

Standard deviation3.4647102
Coefficient of variation (CV)3.9075679
Kurtosis69.277601
Mean0.88666667
Median Absolute Deviation (MAD)0.14
Skewness8.3395578
Sum130.34
Variance12.004217
MonotonicityNot monotonic
2024-11-01T18:01:11.887900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.33 8
 
5.2%
0.23 6
 
3.9%
0.24 5
 
3.2%
0.28 4
 
2.6%
0.36 4
 
2.6%
0.27 4
 
2.6%
0.1 4
 
2.6%
1.2 4
 
2.6%
0.49 3
 
1.9%
0.19 3
 
1.9%
Other values (61) 102
66.2%
(Missing) 7
 
4.5%
ValueCountFrequency (%)
0.1 4
2.6%
0.11 1
 
0.6%
0.12 1
 
0.6%
0.13 2
1.3%
0.14 1
 
0.6%
0.15 1
 
0.6%
0.17 2
1.3%
0.18 2
1.3%
0.19 3
1.9%
0.2 1
 
0.6%
ValueCountFrequency (%)
30.12 2
1.3%
2.8 1
 
0.6%
1.9 1
 
0.6%
1.5 1
 
0.6%
1.4 2
1.3%
1.3 2
1.3%
1.2 4
2.6%
1.1 1
 
0.6%
0.95 1
 
0.6%
0.88 1
 
0.6%

fosf_ortofos_mg_l
Real number (ℝ)

High correlation 

Distinct62
Distinct (%)40.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41545455
Minimum0.1
Maximum2.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-01T18:01:12.219632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1365
Q10.23
median0.33
Q30.4975
95-th percentile0.924
Maximum2.6
Range2.5
Interquartile range (IQR)0.2675

Descriptive statistics

Standard deviation0.30868494
Coefficient of variation (CV)0.74300533
Kurtosis16.932136
Mean0.41545455
Median Absolute Deviation (MAD)0.115
Skewness3.2483309
Sum63.98
Variance0.095286393
MonotonicityNot monotonic
2024-11-01T18:01:12.530320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.32 7
 
4.5%
0.2 7
 
4.5%
0.27 7
 
4.5%
0.54 6
 
3.9%
0.31 6
 
3.9%
0.18 5
 
3.2%
0.39 5
 
3.2%
0.4 5
 
3.2%
0.35 4
 
2.6%
0.24 4
 
2.6%
Other values (52) 98
63.6%
ValueCountFrequency (%)
0.1 4
2.6%
0.11 2
 
1.3%
0.12 1
 
0.6%
0.13 1
 
0.6%
0.14 1
 
0.6%
0.15 3
1.9%
0.16 2
 
1.3%
0.17 2
 
1.3%
0.18 5
3.2%
0.19 3
1.9%
ValueCountFrequency (%)
2.6 1
0.6%
1.4 2
1.3%
1.3 1
0.6%
1.2 2
1.3%
1 1
0.6%
0.95 1
0.6%
0.91 1
0.6%
0.88 1
0.6%
0.86 2
1.3%
0.85 2
1.3%

dbo_mg_l
Real number (ℝ)

High correlation  Missing 

Distinct51
Distinct (%)46.4%
Missing44
Missing (%)28.6%
Infinite0
Infinite (%)0.0%
Mean6.2436364
Minimum1.9
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-01T18:01:12.813055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile1.9
Q12.4
median5
Q37.3
95-th percentile14.55
Maximum42
Range40.1
Interquartile range (IQR)4.9

Descriptive statistics

Standard deviation5.3663328
Coefficient of variation (CV)0.85948837
Kurtosis17.543192
Mean6.2436364
Median Absolute Deviation (MAD)2.55
Skewness3.2708182
Sum686.8
Variance28.797528
MonotonicityNot monotonic
2024-11-01T18:01:13.141909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 16
 
10.4%
5 10
 
6.5%
1.9 10
 
6.5%
12 5
 
3.2%
11 3
 
1.9%
14 3
 
1.9%
5.8 3
 
1.9%
9.4 3
 
1.9%
6.5 3
 
1.9%
5.2 3
 
1.9%
Other values (41) 51
33.1%
(Missing) 44
28.6%
ValueCountFrequency (%)
1.9 10
6.5%
2 16
10.4%
2.3 1
 
0.6%
2.4 2
 
1.3%
2.5 2
 
1.3%
2.6 1
 
0.6%
2.8 1
 
0.6%
3.1 1
 
0.6%
3.3 1
 
0.6%
3.4 1
 
0.6%
ValueCountFrequency (%)
42 1
 
0.6%
21 1
 
0.6%
18 2
 
1.3%
16 1
 
0.6%
15 1
 
0.6%
14 3
1.9%
12 5
3.2%
11 3
1.9%
10 2
 
1.3%
9.4 3
1.9%

dqo_mg_l
Real number (ℝ)

Distinct48
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.188312
Minimum29
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-01T18:01:13.455607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile29
Q130
median30
Q352.25
95-th percentile85.4
Maximum180
Range151
Interquartile range (IQR)22.25

Descriptive statistics

Standard deviation23.477084
Coefficient of variation (CV)0.53129624
Kurtosis8.5813656
Mean44.188312
Median Absolute Deviation (MAD)1
Skewness2.4777461
Sum6805
Variance551.17346
MonotonicityNot monotonic
2024-11-01T18:01:13.753599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
30 54
35.1%
29 27
17.5%
50 7
 
4.5%
39 4
 
2.6%
33 3
 
1.9%
36 3
 
1.9%
48 3
 
1.9%
59 2
 
1.3%
63 2
 
1.3%
80 2
 
1.3%
Other values (38) 47
30.5%
ValueCountFrequency (%)
29 27
17.5%
30 54
35.1%
31 1
 
0.6%
32 2
 
1.3%
33 3
 
1.9%
34 2
 
1.3%
35 1
 
0.6%
36 3
 
1.9%
37 1
 
0.6%
39 4
 
2.6%
ValueCountFrequency (%)
180 1
0.6%
135 1
0.6%
130 1
0.6%
110 1
0.6%
94 1
0.6%
90 1
0.6%
89 1
0.6%
88 1
0.6%
84 1
0.6%
82 2
1.3%

turbiedad_ntu
Real number (ℝ)

Distinct60
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.342857
Minimum2.5
Maximum130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-01T18:01:14.051303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile8.15
Q117
median27.5
Q345
95-th percentile85
Maximum130
Range127.5
Interquartile range (IQR)28

Descriptive statistics

Standard deviation24.213727
Coefficient of variation (CV)0.7050586
Kurtosis1.4434664
Mean34.342857
Median Absolute Deviation (MAD)11.5
Skewness1.3036196
Sum5288.8
Variance586.30456
MonotonicityNot monotonic
2024-11-01T18:01:14.380336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 7
 
4.5%
19 6
 
3.9%
45 6
 
3.9%
22 6
 
3.9%
28 5
 
3.2%
23 5
 
3.2%
90 5
 
3.2%
60 5
 
3.2%
13 5
 
3.2%
26 5
 
3.2%
Other values (50) 99
64.3%
ValueCountFrequency (%)
2.5 1
0.6%
3.3 1
0.6%
4.1 1
0.6%
6 1
0.6%
6.1 1
0.6%
7.3 1
0.6%
7.5 2
1.3%
8.5 1
0.6%
8.6 1
0.6%
8.9 1
0.6%
ValueCountFrequency (%)
130 1
 
0.6%
110 1
 
0.6%
90 5
3.2%
85 2
 
1.3%
84 1
 
0.6%
80 2
 
1.3%
75 3
1.9%
71 1
 
0.6%
70 3
1.9%
67 1
 
0.6%

cr_total_mg_l
Real number (ℝ)

Missing 

Distinct23
Distinct (%)15.2%
Missing3
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean0.55479735
Minimum0.005
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-01T18:01:14.661935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.005
5-th percentile0.005
Q10.005
median0.005
Q30.005
95-th percentile6
Maximum12
Range11.995
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.9638856
Coefficient of variation (CV)3.539825
Kurtosis13.891334
Mean0.55479735
Median Absolute Deviation (MAD)0
Skewness3.7251564
Sum83.7744
Variance3.8568465
MonotonicityNot monotonic
2024-11-01T18:01:14.928295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.005 115
74.7%
0.007 5
 
3.2%
6 4
 
2.6%
0.006 3
 
1.9%
5 3
 
1.9%
0.011 2
 
1.3%
0.0061 2
 
1.3%
7 2
 
1.3%
0.009 1
 
0.6%
0.01 1
 
0.6%
Other values (13) 13
 
8.4%
(Missing) 3
 
1.9%
ValueCountFrequency (%)
0.005 115
74.7%
0.0051 1
 
0.6%
0.006 3
 
1.9%
0.0061 2
 
1.3%
0.0062 1
 
0.6%
0.0064 1
 
0.6%
0.0069 1
 
0.6%
0.007 5
 
3.2%
0.0079 1
 
0.6%
0.008 1
 
0.6%
ValueCountFrequency (%)
12 1
 
0.6%
10 1
 
0.6%
8 1
 
0.6%
7 2
1.3%
6 4
2.6%
5 3
1.9%
0.02 1
 
0.6%
0.015 1
 
0.6%
0.011 2
1.3%
0.01 1
 
0.6%

clorofila_a_ug_l
Real number (ℝ)

Missing  Zeros 

Distinct68
Distinct (%)45.3%
Missing4
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean270.63067
Minimum0
Maximum6410
Zeros5
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-01T18:01:15.211101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.245
Q110
median10
Q346.525
95-th percentile1581.5
Maximum6410
Range6410
Interquartile range (IQR)36.525

Descriptive statistics

Standard deviation826.53116
Coefficient of variation (CV)3.0540928
Kurtosis27.062804
Mean270.63067
Median Absolute Deviation (MAD)1
Skewness4.7987981
Sum40594.6
Variance683153.76
MonotonicityNot monotonic
2024-11-01T18:01:15.523630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 74
48.1%
0 5
 
3.2%
0.3 3
 
1.9%
350 2
 
1.3%
0.2 2
 
1.3%
0.6 2
 
1.3%
70.8 1
 
0.6%
20.7 1
 
0.6%
130.2 1
 
0.6%
42.1 1
 
0.6%
Other values (58) 58
37.7%
(Missing) 4
 
2.6%
ValueCountFrequency (%)
0 5
3.2%
0.1 1
 
0.6%
0.2 2
 
1.3%
0.3 3
1.9%
0.4 1
 
0.6%
0.5 1
 
0.6%
0.6 2
 
1.3%
0.7 1
 
0.6%
0.8 1
 
0.6%
1 1
 
0.6%
ValueCountFrequency (%)
6410 1
0.6%
4650 1
0.6%
3590 1
0.6%
2900 1
0.6%
2500 1
0.6%
2130 1
0.6%
1960 1
0.6%
1730 1
0.6%
1400 1
0.6%
1290 1
0.6%

microcistina_ug_l
Real number (ℝ)

High correlation  Missing 

Distinct7
Distinct (%)4.8%
Missing8
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean0.18568493
Minimum0.15
Maximum1.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-01T18:01:15.774264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.15
5-th percentile0.15
Q10.15
median0.15
Q30.18
95-th percentile0.2
Maximum1.67
Range1.52
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.16046444
Coefficient of variation (CV)0.86417591
Kurtosis59.12294
Mean0.18568493
Median Absolute Deviation (MAD)0
Skewness7.3729289
Sum27.11
Variance0.025748838
MonotonicityNot monotonic
2024-11-01T18:01:15.993865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.15 109
70.8%
0.2 31
 
20.1%
1 2
 
1.3%
0.4 1
 
0.6%
0.3 1
 
0.6%
1.67 1
 
0.6%
0.19 1
 
0.6%
(Missing) 8
 
5.2%
ValueCountFrequency (%)
0.15 109
70.8%
0.19 1
 
0.6%
0.2 31
 
20.1%
0.3 1
 
0.6%
0.4 1
 
0.6%
1 2
 
1.3%
1.67 1
 
0.6%
ValueCountFrequency (%)
1.67 1
 
0.6%
1 2
 
1.3%
0.4 1
 
0.6%
0.3 1
 
0.6%
0.2 31
 
20.1%
0.19 1
 
0.6%
0.15 109
70.8%

ica
Real number (ℝ)

High correlation 

Distinct38
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.071429
Minimum23
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-01T18:01:16.244874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile33
Q138
median42
Q350
95-th percentile59.35
Maximum76
Range53
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.9487335
Coefficient of variation (CV)0.20305068
Kurtosis0.32568788
Mean44.071429
Median Absolute Deviation (MAD)5
Skewness0.65752044
Sum6787
Variance80.079832
MonotonicityNot monotonic
2024-11-01T18:01:16.858042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
40 13
 
8.4%
42 10
 
6.5%
37 10
 
6.5%
36 9
 
5.8%
38 8
 
5.2%
46 7
 
4.5%
41 7
 
4.5%
39 7
 
4.5%
45 7
 
4.5%
55 6
 
3.9%
Other values (28) 70
45.5%
ValueCountFrequency (%)
23 1
 
0.6%
25 1
 
0.6%
29 2
 
1.3%
31 1
 
0.6%
32 2
 
1.3%
33 4
 
2.6%
34 3
 
1.9%
35 5
3.2%
36 9
5.8%
37 10
6.5%
ValueCountFrequency (%)
76 1
 
0.6%
67 1
 
0.6%
64 1
 
0.6%
62 1
 
0.6%
61 2
 
1.3%
60 2
 
1.3%
59 5
3.2%
58 4
2.6%
57 1
 
0.6%
56 2
 
1.3%

Interactions

2024-11-01T18:00:56.842664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:58:52.232841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:03.023966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:08.765268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:15.878197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:22.367576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:36.748081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:48.563901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:00.751010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-01T18:00:12.272896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:17.689023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:23.466405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:28.654914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:34.201020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:40.099846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:45.532616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:51.344398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-01T17:58:52.515858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:03.231885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-01T17:59:22.984979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:37.251382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:49.126879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:00.938469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-01T18:00:12.448310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:17.864547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:23.637246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:28.860409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:34.373670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:40.273360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:45.707113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-01T18:00:34.561863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:40.457882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:45.894435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:51.721920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:57.380228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-01T17:59:38.220555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-01T18:00:07.562463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-01T18:00:40.647958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:46.085694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:51.942375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:57.566895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:58:54.349178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:03.829048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:09.878758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-01T17:59:39.147267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:50.764020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-01T18:00:07.736016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:13.009554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-01T18:00:24.284625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-01T18:00:40.834715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:46.274459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-01T18:00:58.414404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:58:55.905377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-01T17:59:11.114728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:17.924279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-01T18:00:30.371001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-01T18:00:47.137201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-01T17:58:59.039810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-01T17:59:28.054899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-01T17:59:01.296360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:07.244012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:14.344490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:20.659373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:32.467654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:44.664995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:56.883837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:04.954970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:10.924669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:16.275549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:22.069153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:27.221407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:32.770717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:38.683211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:44.087393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:49.522449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:55.399156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:01:00.927855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:01.589100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:07.494950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:14.602708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:20.883012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:33.043297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:45.109210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:57.395519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:05.221467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:11.131789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:16.480325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:22.270955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:27.428916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:32.988272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:38.903264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:44.277275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:49.724341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:55.614217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:01:01.117031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:01.882154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:07.714624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:14.838257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:21.096368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:33.695530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:45.627312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:57.991938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:05.479213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:11.329285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:16.679277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:22.478496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:27.646552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:33.194526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:39.110256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:44.497020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:49.945605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:55.835337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:01:01.303381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:02.147196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:07.953159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:15.058888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:21.336451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:34.317431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:46.145200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:58.531003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:05.708975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:11.520146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:16.890406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:22.663242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:27.837841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:33.396783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:39.298829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:44.716867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:50.134749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:56.039982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:01:01.496873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:02.394052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:08.146712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:15.283252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:21.579068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:34.932860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:46.654847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:59.074281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:05.934817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:11.706561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:17.079534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:22.867482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:28.059188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:33.598955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:39.499219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:44.939656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:50.779378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:56.244207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:01:01.682592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:02.600867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:08.343026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:15.469876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:21.857109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:35.526352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:47.509774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:59.639968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:06.154042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:11.898544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:17.286704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:23.056530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:28.263184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:33.804560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:39.688906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:45.140433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:50.964433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:56.463926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:01:01.871060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:02.838978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:08.548523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:15.704280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:22.143761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:36.184593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:59:48.058289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:00.219302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:06.370449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:12.114871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:17.504389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:23.259413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:28.480463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:34.011400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:39.909155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:45.345663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:51.188483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T18:00:56.672637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-01T18:01:17.091572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
clorofila_a_ug_lcolif_fecales_ufc_100mlcolorcr_total_mg_ldbo_mg_ldqo_mg_lenteroc_ufc_100mlescher_coli_ufc_100mlespumasfosf_ortofos_mg_licamat_suspmicrocistina_ug_lnh4_mg_lnitrato_mg_lodoloresp_total_l_mg_lphsitiostem_aguatem_aireturbiedad_ntu
clorofila_a_ug_l1.0000.1470.0000.0160.333-0.077-0.260-0.3780.0000.178-0.2940.0000.1290.0070.3940.1960.0000.2070.3160.000-0.330-0.055-0.088
colif_fecales_ufc_100ml0.1471.0000.2100.2010.3430.2100.2680.2260.1780.330-0.6030.093-0.2190.3790.230-0.2930.1980.342-0.1560.000-0.3130.088-0.150
color0.0000.2101.0000.0000.0000.0000.1030.0000.3990.1860.3630.2470.2280.3540.0000.3210.4730.0000.2650.5070.3210.1980.000
cr_total_mg_l0.0160.2010.0001.0000.2320.1850.0340.1170.0000.299-0.3450.000-0.2710.117-0.044-0.0710.0000.401-0.0410.000-0.195-0.2260.151
dbo_mg_l0.3330.3430.0000.2321.0000.000-0.071-0.0540.2430.516-0.5070.000-0.1120.3200.1610.1010.3310.5550.2960.184-0.129-0.139-0.326
dqo_mg_l-0.0770.2100.0000.1850.0001.0000.1640.2610.0000.010-0.1830.000-0.347-0.0610.1830.0220.0000.106-0.0020.057-0.269-0.2830.331
enteroc_ufc_100ml-0.2600.2680.1030.034-0.0710.1641.0000.4820.000-0.042-0.1330.2250.165-0.010-0.034-0.2270.0850.044-0.0810.0930.1210.1820.036
escher_coli_ufc_100ml-0.3780.2260.0000.117-0.0540.2610.4821.0000.411-0.031-0.0680.410-0.1210.029-0.116-0.2250.3890.137-0.0840.0880.158-0.0110.067
espumas0.0000.1780.3990.0000.2430.0000.0000.4111.0000.3680.3380.2400.0000.4610.0000.0000.4190.0000.1490.5250.2090.0000.000
fosf_ortofos_mg_l0.1780.3300.1860.2990.5160.010-0.042-0.0310.3681.000-0.5030.0000.1440.2980.039-0.2520.3450.8320.1420.000-0.0700.080-0.404
ica-0.294-0.6030.363-0.345-0.507-0.183-0.133-0.0680.338-0.5031.0000.2700.179-0.494-0.1560.2580.560-0.5740.0020.0000.3200.0440.277
mat_susp0.0000.0930.2470.0000.0000.0000.2250.4100.2400.0000.2701.0000.1200.1520.0000.3000.2690.0000.2680.4570.3100.2510.000
microcistina_ug_l0.129-0.2190.228-0.271-0.112-0.3470.165-0.1210.0000.1440.1790.1201.000-0.367-0.106-0.1360.0000.0890.0240.0000.6650.657-0.077
nh4_mg_l0.0070.3790.3540.1170.320-0.061-0.0100.0290.4610.298-0.4940.152-0.3671.000-0.070-0.2770.3840.300-0.1340.168-0.255-0.160-0.452
nitrato_mg_l0.3940.2300.000-0.0440.1610.183-0.034-0.1160.0000.039-0.1560.000-0.106-0.0701.0000.1810.0000.0000.1550.000-0.2570.013-0.029
od0.196-0.2930.321-0.0710.1010.022-0.227-0.2250.000-0.2520.2580.300-0.136-0.2770.1811.0000.067-0.2830.6130.161-0.273-0.1390.238
olores0.0000.1980.4730.0000.3310.0000.0850.3890.4190.3450.5600.2690.0000.3840.0000.0671.0000.0000.2470.2620.0000.0000.150
p_total_l_mg_l0.2070.3420.0000.4010.5550.1060.0440.1370.0000.832-0.5740.0000.0890.3000.000-0.2830.0001.0000.1120.000-0.056-0.013-0.367
ph0.316-0.1560.265-0.0410.296-0.002-0.081-0.0840.1490.1420.0020.2680.024-0.1340.1550.6130.2470.1121.0000.205-0.218-0.2120.003
sitios0.0000.0000.5070.0000.1840.0570.0930.0880.5250.0000.0000.4570.0000.1680.0000.1610.2620.0000.2051.0000.0000.0000.081
tem_agua-0.330-0.3130.321-0.195-0.129-0.2690.1210.1580.209-0.0700.3200.3100.665-0.255-0.257-0.2730.000-0.056-0.2180.0001.0000.706-0.001
tem_aire-0.0550.0880.198-0.226-0.139-0.2830.182-0.0110.0000.0800.0440.2510.657-0.1600.013-0.1390.000-0.013-0.2120.0000.7061.000-0.107
turbiedad_ntu-0.088-0.1500.0000.151-0.3260.3310.0360.0670.000-0.4040.2770.000-0.077-0.452-0.0290.2380.150-0.3670.0030.081-0.001-0.1071.000

Missing values

2024-11-01T18:01:02.181664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-01T18:01:02.903328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-01T18:01:03.486311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

sitiosfechatem_aguatem_aireodpholorescolorespumasmat_suspcolif_fecales_ufc_100mlescher_coli_ufc_100mlenteroc_ufc_100mlnitrato_mg_lnh4_mg_lp_total_l_mg_lfosf_ortofos_mg_ldbo_mg_ldqo_mg_lturbiedad_ntucr_total_mg_lclorofila_a_ug_lmicrocistina_ug_lica
0Canal Villanueva y Río Luján2022-02-2324.523.35.306.56FalseFalseFalseTrue22001001302.90.4200.230.156.22990.00.00510.00.255.0
1Río Lujan y Arroyo Caraguatá2022-02-2325.423.32.256.56TrueTrueFalseFalse12002004003.30.5100.410.355.82934.00.00510.00.242.0
2Canal Aliviador y Río Lujan2022-02-2324.623.32.946.59FalseTrueFalseFalse18002005806.50.0500.590.541.92917.00.00510.00.245.0
3Río Carapachay y Arroyo Gallo Fiambre2022-02-2325.223.32.227.45TrueTrueFalseFalse14001003007.41.0000.380.405.82923.00.00510.00.246.0
4Río Reconquista y Río Lujan2022-02-2324.120.01.026.39FalseTrueFalseTrue11001003708.80.0490.550.542.65918.00.00510.00.244.0
5Rio Tigre 100m antes del Rio Luján2022-02-2324.923.33.506.53FalseFalseFalseTrue32002007504.43.5001.100.913.91308.90.00510.00.240.0
6Río Lujan y Canal San Fernando2022-02-2324.520.01.506.54FalseTrueFalseTrue1800015001005.62.0000.730.603.54212.00.00510.00.435.0
7Río Capitán y Río San Antonio2022-02-2324.521.06.306.48FalseTrueFalseFalse100020012003.10.0490.170.165.56990.00.00510.00.246.0
8Arroyo Abra Vieja y Santa Rosa2022-02-2323.421.04.496.76FalseFalseFalseFalse4001002201.90.1000.210.191.92939.00.00510.00.258.0
9Del Arca2022-02-2321.523.03.856.66FalseFalseFalseTrue22001002705.40.0490.280.391.92928.00.00510.00.251.0
sitiosfechatem_aguatem_aireodpholorescolorespumasmat_suspcolif_fecales_ufc_100mlescher_coli_ufc_100mlenteroc_ufc_100mlnitrato_mg_lnh4_mg_lp_total_l_mg_lfosf_ortofos_mg_ldbo_mg_ldqo_mg_lturbiedad_ntucr_total_mg_lclorofila_a_ug_lmicrocistina_ug_lica
158Boca Cerrada (Res.Nat. Punta Lara)2022-10-31NaN10.0NaNNaNFalseFalseFalseFalse150100306.20.140.600.38NaN7239.010.00074.20.1541.0
159Camping Eva Perón2022-10-3116.07.011.059.17FalseFalseFalseFalse21080905.90.150.360.21NaN3331.00.00536.50.1938.0
160Toma de agua Club de Pesca2022-10-3117.26.08.388.09FalseFalseFalseFalse9535505.70.410.290.23NaN4626.00.00529.40.1541.0
161Arroyo El GatoNaT18.04.07.367.87FalseFalseFalseFalse8007002205.42.300.360.23NaN3023.00.00516.70.1537.0
162Ensenada Prefectura Isla SantiagoNaT17.15.08.988.05FalseFalseFalseFalse13030456.10.400.240.24NaN3039.00.0050.60.1554.0
163Balneario Palo Blanco2022-10-3110.012.0NaNNaNFalseFalseFalseTrue8006004006.90.380.240.24NaN3023.00.0052.10.1543.0
164Diagonal 66 (descarga cloaca)2022-10-3110.012.0NaNNaNFalseTrueFalseTrue8000080000120005.21.2030.120.39NaN3118.20.00520.20.1537.0
165Playa La Bagliardi2022-10-3110.012.0NaNNaNFalseFalseFalseTrue140010003804.60.800.450.43NaN3040.00.0050.20.1549.0
166Balneario Municipal2022-10-3110.012.0NaNNaNFalseFalseFalseTrue180015005005.20.550.270.27NaN3990.05.00010.50.1539.0
167Playa La Balandra2022-10-3110.012.0NaNNaNFalseFalseFalseTrue9006004805.10.210.480.35NaN3070.05.00048.00.1534.0